Background: Mental health treatment is hindered by the limited number of mental health care providers and the infrequency of care. Digital mental health technology can help supplement treatment by remotely monitoring patient symptoms and predicting mental health crises in between clinical visits. However, the feasibility of digital mental health technologies has not yet been sufficiently explored. Rhythms, from the company Health Rhythms, is a smartphone platform that uses passively acquired smartphone data with artificial intelligence and predictive analytics to alert patients and providers to an emerging mental health crisis.
Objective: The objective of this study was to test the feasibility and acceptability of Rhythms among patients attending an academic psychiatric outpatient clinic.
Methods: Our group embedded Rhythms into the electronic health record of a large health system. Patients with a diagnosis of major depressive disorder, bipolar disorder, or other mood disorder were contacted online and enrolled for a 6-week trial of Rhythms. Participants provided data by completing electronic surveys as well as by active and passive use of Rhythms. Emergent and urgent alerts were monitored and managed according to passively collected data and patient self-ratings. A purposively sampled group of participants also participated in qualitative interviews about their experience with Rhythms at the end of the study.
Results: Of the 104 participants, 89 (85.6%) completed 6 weeks of monitoring. The majority of the participants were women (72/104, 69.2%), White (84/104, 80.8%), and non-Hispanic (100/104, 96.2%) and had a diagnosis of major depressive disorder (71/104, 68.3%). Two emergent alerts and 19 urgent alerts were received and managed according to protocol over 16 weeks. More than two-thirds (63/87, 72%) of those participating continued to use Rhythms after study completion. Comments from participants indicated appreciation for greater self-awareness and provider connection, while providers reported that Rhythms provided a more nuanced understanding of patient experience between clinical visits.
Conclusions: Rhythms is a user-friendly, electronic health record-adaptable, smartphone-based tool that provides patients and providers with a greater understanding of patient mental health status. Integration of Rhythms into health systems has the potential to facilitate mental health care and improve the experience of both patients and providers.
Background: There is a growing concern that digital health care may exacerbate existing health disparities. Digital health care or eHealth encompasses the digital apps that are used in health care. Differences in access, use, and perceived benefits of digital technology among socioeconomic groups are commonly referred to as the digital divide. Current research shows that people in lower socioeconomic positions (SEPs) use eHealth less frequently.
Objective: This study aims to (1) investigate the association between SEP and eHealth access to, use of, and perceived benefit within the adult Dutch population and (2) evaluate disparities in eHealth access, use, and perceived benefit through three socioeconomic variables-education, standardized income, and the socioeconomic status of the neighborhood.
Methods: A secondary analysis was conducted on data from the Nivel Dutch Health Care Consumer Panel (response rate 57%, 849/1500), to assess access to, use of, and perceived benefits from eHealth. These data were collected to monitor eHealth developments in the Netherlands. eHealth was examined through two concepts: (1) eHealth in general and (2) websites, apps, and wearables. Results were stratified into 9 SEP populations based on 3 indicators-education, standardized income, and socioeconomic status level of the neighborhood. Logistic regression analyses were performed to evaluate whether the outcomes varied significantly across different SEP groups. Age was included as a covariate to control for confounding.
Results: This study confirms the association between eHealth and SEP and shows that low SEP respondents have less access (odds ratio [OR] 5.72, 95% CI 3.06-10.72) and use (OR 4.96, 95% CI 2.66-9.24) of eHealth compared to medium or high SEP respondents. Differences were most profound when stratifying for levels of education.
Conclusions: The access to and use of eHealth has a socioeconomic gradient and emphasizes that SEP indicators cannot be used interchangeably to assess eHealth access and use. The results underline the importance of activities and policies aimed at improving eHealth accessibility and usage among low SEP groups to mitigate disparities in health between different socioeconomic groups.
Background: Many efforts to increase the uptake of e-mental health (eMH) have failed due to a lack of knowledge and skills, particularly among professionals. To train health care professionals in technology, serious gaming concepts such as educational escape rooms are increasingly used, which could also possibly be used in mental health care. However, such serious-game concepts are scarcely available for eMH training for mental health care professionals.
Objective: This study aims to co-design an escape room for training mental health care professionals' eMH skills and test the escape room's usability by exploring their experiences with this concept as a training method.
Methods: This project used a research through design approach with 3 design stages. In the first stage, the purpose, expectations, and storylines for the escape room were formulated in 2 co-design sessions with mental health care professionals, game designers, innovation staff, and researchers. In the second stage, the results were translated into the first escape room, which was tested in 3 sessions, including one web version of the escape room. In the third stage, the escape room was tested with mental health care professionals outside the co-design team. First, 2 test sessions took place, followed by 3 field study sessions. In the field study sessions, a questionnaire was used in combination with focus groups to assess the usability of the escape room for eMH training in practice.
Results: An escape room prototype was iteratively developed and tested by the co-design team, which delivered multiple suggestions for adaptations that were assimilated in each next version of the prototype. The field study showed that the escape room creates a positive mindset toward eMH. The suitability of the escape room to explore the possibilities of eMH was rated 4.7 out of 5 by the professionals who participated in the field study. In addition, it was found to be fun and educational at the same time, scoring 4.7 (SD 0.68) on a 5-point scale. Attention should be paid to the game's complexity, credibility, and flexibility. This is important for the usefulness of the escape room in clinical practice, which was rated an average of 3.8 (SD 0.77) on a 5-point scale. Finally, implementation challenges should be addressed, including organizational policy and stimulation of eMH training.
Conclusions: We can conclude that the perceived usability of an escape room for training mental health care professionals in eMH skills is promising. However, it requires additional effort to transfer the learnings into mental health care professionals' clinical practice. A straightforward implementation plan and testing the effectiveness of an escape room on skill enhancement in mental health care professionals are essential next steps to reach sustainable goals.
Background: Primary health care (PHC) services face operational challenges due to high patient volumes, leading to complex management needs. Patients access services through booked appointments and walk-in visits, with walk-in visits often facing longer waiting times. No-show appointments are significant contributors to inefficiency in PHC operations, which can lead to an estimated 3%-14% revenue loss, disrupt resource allocation, and negatively impact health care quality. Emirates Health Services (EHS) PHC centers handle over 140,000 visits monthly. Baseline data indicate a 21% no-show rate and an average patient wait time exceeding 16 minutes, necessitating an advanced scheduling and resource management system to enhance patient experiences and operational efficiency.
Objective: The objective of this study was to evaluate the impact of an artificial intelligence (AI)-driven solution that was integrated with an interactive real-time data dashboard on reducing no-show appointments and improving patient waiting times at the EHS PHCs.
Methods: This study introduced an innovative AI-based data application to enhance PHC efficiency. Leveraging our electronic health record system, we deployed an AI model with an 86% accuracy rate to predict no-shows by analyzing historical data and categorizing appointments based on no-show risk. The model was integrated with a real-time dashboard to monitor patient journeys and wait times. Clinic coordinators used the dashboard to proactively manage high-risk appointments and optimize resource allocation. The intervention was assessed through a before-and-after comparison of PHC appointment dynamics and wait times, analyzing data from 135,393 appointments (67,429 before implementation and 67,964 after implementation).
Results: Implementation of the AI-powered no-show prediction model resulted in a significant 50.7% reduction in no-show rates (P<.001). The odds ratio for no-shows after implementation was 0.43 (95% CI 0.42-0.45; P<.001), indicating a 57% reduction in the likelihood of no-shows. Additionally, patient wait times decreased by an average of 5.7 minutes overall (P<.001), with some PHCs achieving up to a 50% reduction in wait times.
Conclusions: This project demonstrates that integrating AI with a data analytics platform and an electronic health record systems can significantly improve operational efficiency and patient satisfaction in PHC settings. The AI model enabled daily assessments of wait times and allowed for real-time adjustments, such as reallocating patients to different clinicians, thus reducing wait times and optimizing resource use. These findings illustrate the transformative potential of AI and real-time data analytics in health care delivery.
Background: The iAide2 (Tokai) physical activity monitoring system includes diverse measurements and wireless features useful to researchers. The iAide2's sleep measurement capabilities have not been compared to validated sleep measurement standards in any published work.
Objective: We aimed to assess the iAide2's sleep duration and total sleep time (TST) measurement performance and perform calibration if needed.
Methods: We performed free-living sleep monitoring in 6 convenience-sampled participants without known sleep disorders recruited from within the Waki DTx Laboratory at the Graduate School of Medicine, University of Tokyo. To assess free-living sleep, we validated the iAide2 against a second actigraph that was previously validated against polysomnography, the MotionWatch 8 (MW8; CamNtech Ltd). The participants wore both devices on the nondominant arm, with the MW8 closest to the hand, all day except when bathing. The MW8 and iAide2 assessments both used the MW8 EVENT-marker button to record bedtime and risetime. For the MW8, MotionWare Software (version 1.4.20; CamNtech Ltd) provided TST, and we calculated sleep duration from the sleep onset and sleep offset provided by the software. We used a similar process with the iAide2, using iAide2 software (version 7.0). We analyzed 64 nights and evaluated the agreement between the iAide2 and the MW8 for sleep duration and TST based on intraclass correlation coefficients (ICCs).
Results: The absolute ICCs (2-way mixed effects, absolute agreement, single measurement) for sleep duration (0.69, 95% CI -0.07 to 0.91) and TST (0.56, 95% CI -0.07 to 0.82) were moderate. The consistency ICC (2-way mixed effects, consistency, single measurement) was excellent for sleep duration (0.91, 95% CI 0.86-0.95) and moderate for TST (0.78, 95% CI 0.67-0.86). We determined a simple calibration approach. After calibration, the ICCs improved to 0.96 (95% CI 0.94-0.98) for sleep duration and 0.82 (95% CI 0.71-0.88) for TST. The results were not sensitive to the specific participants included, with an ICC range of 0.96-0.97 for sleep duration and 0.79-0.87 for TST when applying our calibration equation to data removing one participant at a time and 0.96-0.97 for sleep duration and 0.79-0.86 for TST when recalibrating while removing one participant at a time.
Conclusions: The measurement errors of the uncalibrated iAide2 for both sleep duration and TST seem too large for them to be useful as absolute measurements, though they could be useful as relative measurements. The measurement errors after calibration are low, and the calibration approach is general and robust, validating the use of iAide2's sleep measurement functions alongside its other features in physical activity research.
Background: As digitalization continues to advance globally, the health care sector, including dental practice, increasingly recognizes social media as a vital tool for health care promotion, patient recruitment, marketing, and communication strategies.
Objective: This study aimed to investigate the use of social media and assess its impact on enhancing dental care and practice among dental professionals in the Philippines.
Methods: A cross-sectional survey was conducted among dental practitioners in the Philippines. The study used a 23-item questionnaire, which included 5 questions on dentists' background and demographic information and 18 questions regarding the use, frequency, and purpose of social media in patient advising and quality of care improvement. Data were analyzed using SPSS software, with frequency distributions and χ2 tests used to assess the association between social media use and demographic variables and the impact on dental practice.
Results: The 265 dental practitioners in this study were predominantly female (n=204, 77%) and aged between 20-30 years (n=145, 54.7%). Most of the participants were general practitioners (n=260, 98.1%) working in a private practice (n=240, 90.6%), with 58.5% (n=155) having 0-5 years of clinical experience. Social media use was significantly higher among younger practitioners (20-30 years old) compared to older age groups (P<.001), though factors such as sex, dental specialty, and years of clinical practice did not significantly influence use. The majority (n=179, 67.5%) reported using social media in their practice, primarily for oral health promotion and education (n=191, 72.1%), connecting with patients and colleagues (n=165, 62.3%), and marketing (n=150, 56.6%). Facebook (n=179, 67.5%) and YouTube (n=163, 61.5%) were the most frequented platforms for clinical information, with Twitter (subsequently rebranded X) being the least used (n=4, 1.5%). Despite widespread social media engagement, only 8.7% (n=23) trusted the credibility of web-based information, and 63.4% (n=168) perceived a potential impact on the patient-dentist relationship due to patients seeking information on the internet. Social media was also perceived to enhance practice quality, with users reporting significant improvements in patient care (P=.001).
Conclusions: The findings highlight that social media is widely used among younger dental practitioners, primarily for education, communication, and marketing purposes. While social media use is associated with perceived improvements in practice quality and patient care, trust in information on social media remains low, and concerns remain regarding its effect on patient relationships. It is recommended to establish enhanced guidelines and provide reliable web-based resources to help dental practitioners use social media effectively and responsibly.
Background: The COVID-19 pandemic has caused serious health, economic, and social consequences worldwide. Understanding how infectious diseases spread can help mitigate these impacts. The Theil index, a measure of inequality rooted in information theory, is useful for identifying geographic disproportionality in COVID-19 incidence across regions.
Objective: This study focused on capturing the degrees of regional disproportionality in incidence rates of infectious diseases over time. Using the Theil index, we aim to assess regional disproportionality in the spread of COVID-19 and detect epicenters where the number of infected individuals was disproportionately concentrated.
Methods: To quantify the degree of disproportionality in the incidence rates, we applied the Theil index to the publicly available data of daily confirmed COVID-19 cases in the United States over a 1100-day period. This index measures relative disproportionality by comparing daily regional case distributions with population proportions, thereby identifying regions where infections are disproportionately concentrated.
Results: Our analysis revealed a dynamic pattern of regional disproportionality in the confirmed cases by monitoring variations in regional contributions to the Theil index as the pandemic progressed. Over time, the index reflected a transition from localized outbreaks to widespread transmission, with high values corresponding to concentrated cases in some regions. We also found that the peaks in the Theil index often preceded surges in confirmed cases, suggesting its potential utility as an early warning signal.
Conclusions: This study demonstrated that the Theil index is one of the effective indices for quantifying regional disproportionality in COVID-19 incidence rates. Although the Theil index alone cannot fully capture all aspects of pandemic dynamics, it serves as a valuable tool when used alongside other indicators such as infection and hospitalization rates. This approach allows policy makers to monitor regional disproportionality efficiently, offering insights for early intervention and targeted resource allocation.
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